| Challenge: | Existing approaches to learn dialogue management only predict one action per turn, limiting expressive power of the conversational agent and introducing unnecessary turns of interactions. |
| Approach: | They propose a model based on a recurrent cell called gated Continue-Act-Slots that overcomes the limitations of existing models and proposes a novel policy model that predicts multiple acts for each turn. |
| Outcome: | The proposed model outperforms existing models on the task of predicting multiple acts for each turn. |
Similar Papers
A Practical Dialogue-Act-Driven Conversation Model for Multi-Turn Response Selection (D19-1)
Copied to clipboard
| Challenge: | Dialogue acts are important in conversation modeling, but they are rarely available for new conversations. |
| Approach: | They propose an end-to-end multi-task model that integrates dialogue acts with context and response in a crossway fashion. |
| Outcome: | The proposed model improves the accuracy of the dialogue act prediction task and the MRR for the response selection task. |
Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing goal-oriented dialogue datasets focus on identifying slots and values, but in reality, customer service agents follow multi-step procedures derived from explicit company policies. |
| Approach: | They propose to use a fully-labeled dataset to study customer service dialogue systems in real-world scenarios. |
| Outcome: | The proposed dataset outperforms existing models but still lacks 50.8% absolute accuracy to reach human-level performance on the dataset. |
Few-Shot Structured Policy Learning for Multi-Domain and Multi-Task Dialogues (2023.findings-eacl)
Copied to clipboard
| Challenge: | Reinforcement learning is widely adopted to model dialogue managers in task-oriented dialogues, but the user simulator provided by state-of-the-art dialogue frameworks are only rough approximations of human behaviour. |
| Approach: | They propose to use structured policies to improve sample efficiency when learning on multi-domain and multi-task environments. |
| Outcome: | The proposed policies improve sample efficiency and performance on multi-domain and multi-task environments. |
Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Tracking (2022.acl-long)
Copied to clipboard
| Challenge: | Experimental results show that task-oriented dialogue systems have attracted growing attention and achieved substantial progress. |
| Approach: | They propose a method that dynamically selects relevant dialogue contents for each slot . they retrieve turn-level utterances and evaluate their relevance to the slot from three perspectives . |
| Outcome: | The proposed method achieves state-of-the-art performance on MultiWOZ 2.1 and MultiWOz 2.2 and superior performance on multiple mainstream benchmark datasets. |
Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users (2023.findings-emnlp)
Copied to clipboard
Yohan Jo, Xinyan Zhao, Arijit Biswas, Nikoletta Basiou, Vincent Auvray, Nikolaos Malandrakis, Angeliki Metallinou, Alexandros Potamianos
| Challenge: | a dataset of task-oriented dialogues assume conversations between the agent and one user at a time . but multi-user task-orientated dialogues are richer, containing deliberation and deliberations . a novel task is proposed to rewrite a task-focused query that retains only task-relevant information . |
| Approach: | They propose to rewrite a task-oriented chat between two users as a concise task-orientated query that retains only task-relevant information and is directly consumable by the dialogue system. |
| Outcome: | The proposed method surpasses existing models on multi-user dialogues and generalizes to unseen domains. |
Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward Decomposition (2020.acl-main)
Copied to clipboard
| Challenge: | Many studies have applied reinforcement learning to train a dialog policy . but modeling a real-world user simulator is challenging and requires domain expertise . |
| Approach: | They propose to build dialog policies with two agents as dialog agents to avoid building a user simulator beforehand. |
| Outcome: | The proposed method can build a system policy and a user policy simultaneously . it can achieve high task success rate through conversational interaction . |
One Planner To Guide Them All ! Learning Adaptive Conversational Planners for Goal-oriented Dialogues (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for goal-oriented dialogues involve training separate models for specific combinations of objectives, leading to computational and scalability issues. |
| Approach: | They propose a new dialogue policy method that can adapt to varying objective preferences at inference time without retraining. |
| Outcome: | The proposed method can adapt to varying objective preferences at inference time without retraining. |
Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systems (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Dialogue policy learning for task-oriented dialogue systems has enjoyed great progress through using reinforcement learning methods. |
| Approach: | They propose a dialogue action decoder and a simulator-free adversarial learning method to improve dialogue agent performance without using reinforcement learning. |
| Outcome: | The proposed methods achieve more stable and higher performance with fewer efforts, such as the domain knowledge required to design a user simulator and the intractable parameter tuning in reinforcement learning. |
Do LLMs Understand Dialogues? A Case Study on Dialogue Acts (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have shown remarkable performance on many unseen tasks in a zero-shot setting. |
| Approach: | They propose to identify three key pre-tasks essential for accurate DA prediction: Turn Management, Communicative Function Identification, and Dialogue Structure Prediction. |
| Outcome: | The proposed model fails to outperform basic rule-based tasks on three key pre-tasks, and the results suggest that the model is flawed. |
Beyond Single-User Dialogue: Assessing Multi-User Dialogue State Tracking Capabilities of Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models have demonstrated remarkable performance in zero-shot dialogue state tracking (DST), reducing the need for task-specific training. |
| Approach: | They extend existing DST dataset by generating utterances of a second user based on speech act theory. |
| Outcome: | The proposed model incorporates utterances of a second user into conversations, enabling a controlled evaluation of LLMs in multi-user settings. |